where \(\phi \)  represents the pre-trained classification network and \(\phi _{(l)}(I_{HQ})\)  represents the high-level representation extracted from the \(l\)  layer of the network. \(h_{l}\) , \(w_{l}\) , and \(c_{l}\)  are the height, width, and the number of channels of the feature map in the \(l\) th layer respectively. With this method, the visual effects of these two images can be as consistent as possible. Among them, VGG [1]} and ResNet [2]} are the most commonly used pre-training classification networks.
